Positron emission tomography (PET), also known as PET imaging, is a type of nuclear medicine imaging that uses radioactive material, placed in a patient's body, to identify molecular activity and processes and, thus, assist in diagnosing disease, evaluating medical conditions, monitoring a patient's response to therapeutic interventions, etc. PET imaging may be accomplished through the use of stand-alone PET imaging systems or, as is more often the case today, hybrid MR-PET or CT-PET imaging systems which work to enhance the quality of the visualization of the acquired PET imaging data.
As shown in FIG. 1, a PET imaging system 10 generally comprises an imaging device (i.e., a scanner) 12 that can detect radioactive emissions from the radioactive material (also known as radiopharmaceuticals or radiotracers) in the internal body P area under examination, a data processor 14 that analyzes the detected emissions information, and an image processor 16 (which in some configurations may be part of the data processor 14) that converts the processed data into image data or images of the area under examination via mathematical image reconstruction software. A user interface 18 (which typically includes an associated display) accompanies the processors 14, 16 and controls the operation of the system 10 and the various components. Although not shown in detail, the various components are operably connected to one another via appropriate control circuitry which is manipulated via the user interface 18. The PET imaging system 10 may be realized as a stand-alone PET imaging system or as part of a larger hybrid MR-PET or CT-PET imaging system.
In operation, after an appropriate radiotracer is placed into a patient's body P and becomes concentrated in tissues of interest, the patient is placed in the central opening of the scanner 12. The radiotracer undergoes positron emission decay and each emitted positron travels in the tissue for a short distance until it interacts with an electron. The encounter annihilates both electron and positron, producing a pair of annihilation (gamma) photons γ moving in approximately opposite directions. The two photons γ travel to respective scintillation detectors that are diametrically opposed within a scintillation detector ring (not visible) forming a part of the central opening. Each photon γ first enters and travels through the respective scintillation detector that converts high-energy photons into electrical signals via a scintillation process. The data processor 14 analyzes the electrical signals and localizes the temporal photon pair along the straight line that joins the two opposed detectors (known as a line of coincidence or a line of response (LOR)). Each scan produces thousands of LORs which form a fan-beam over many angles. The data processor 14 forwards all LOR data to the image processor 16 where final image data is produced via mathematical image reconstruction algorithms and software. Briefly, a map of the sources of the temporal photon pairs may be constructed by solving sets of simultaneous equations for the total activities along the LORs. The resulting image map shows the tissues in which the radiotracer has become concentrated, and can be interpreted by an appropriate health professional.
The annihilation (gamma) photons γ travel through different layers of the patient's body P before reaching the scintillation detectors of the scanner 12. The interaction of the photons γ with the body layers results in an attenuation of the energy of each photon γ. The degree of energy attenuation varies depending upon the body P area under examination. The energy attenuation is sufficient to distort the analysis and resulting LOR data by the data processor 14 and the reconstruction of the body P images by the image processor 16. This can adversely affect the image interpretation and any medical diagnoses by the health professional. To correct for the attenuation, the patient normally undergoes CT imaging to accompany the PET imaging (e.g., as part of a hybrid CT-PET imaging system) so that data may be acquired that can generate attenuation correction data. The attenuation correction data may take on the form of data maps that are subsequently utilized by the PET system 10 to adjust the PET image reconstruction of the body P area under examination. This is described in more detail in an article by H. Zaidi and B. Hasegawa, entitled “Determination of the Attenuation Map in Emission Tomography”, The Journal of Nuclear Medicine, Official Publication Society of Nuclear Medicine, 2003, pp. 291-315, Vol. 44(2).
It would be more advantageous to generate PET attenuation correction maps from MR imaging (e.g., as part of a hybrid MR-PET imaging system) so, among other reasons, the patient can avoid harmful ionizing radiation used by CT imaging. However, MR imaging does not normally produce images with clearly defined bone segmentation, which is essential to performing accurate attenuation correction of PET imaging data.
Bone segmentation has many applications in medical imaging, from clinical research to diagnostics to surgery. Since manual segmentation by an expert is time-consuming and presents issues of intra and inter-reliability, different methods of automatic labeling have been developed during the last decade. On MR images, intensity is similar in bones and air (i.e., air-filled body cavities), therefore a segmentation relying solely on the image is not feasible. To overcome this issue, atlas-based methods have been used, combining intensity and spatial information (this is described in an article by D. V. Iosifescu, M. E. Shenton, S. K. Warfield, R. Kikinis, J. Dengler. F. A. Jolesz, and R. W. McCarley, entitled “An Automated Registration Algorithm for Measuring MRI Subcortical Brain Structures”, NeuroImage, 1997, pp. 13-25, Vol. 6(1)). Atlases are reference/template patients or subjects (also called “textbook subjects”) for whom MR and CT scans are available and which have undergone bone segmentation based on the CT scans. An atlas' MR images and segmentation labels are then registered to the patient under examination (also called herein the “test subject”) whose segmentation is to be produced.
Atlas-based methods, where one or several template structural MR scans are paired with their corresponding segmentations to form the atlases, have been used for a few years (see, for example, the above-cited Iosifescu et al. article). It also has been shown that using multiple atlases improves accuracy (see, for example, articles by C. Svarer, K. Madsen, S. G. Hasselbalch, L. H. Pinborg, S. Haugbøl, V. G. Frøkjaer, S. Holm, O. B. Paulson, and G. M. Knudsen, entitled “MR-based Automatic Delineation of Volumes of Interest in Human Brain PET Images using Probability Maps”, Neuroimage, 2005, pp. 969-979, Vol. 24(4); T. Rohlfing, D. B. Russakoff, and C. R. Maurer, entitled “Expectation Maximization Strategies for Multi-atlas Multi-label Segmentation”, Information Processing in Medical Imaging Proceedings of the Conference, 2003, pp. 210-221, Vol. 18; and T. Rohlfing, R. Brandt, R. Menzel, D. B. Russakoff, and C. R. Maurer, Jr., entitled “Quo Vadis, Aatlas-based Segmentation?”, Strategies, 2005, pp. 435-486, Vol. 3(2)). In all of these atlas-based methods, the assumption is that registration of the test subject MR scan to a template MR scan works well enough that a voxel in the former can be assigned the segmentation label of the corresponding voxel in the latter.
However, this assumption may not hold. For example, in the case of the head/brain examination, the perfect bijection from one head to another doesn't exist due to a large neuroanatomical variability. In such cases, single voxels may be replaced by 3D regions or patches of voxels (usually in the form of a cube) which convey more information. These patch-based atlas methods thus map a patch around a voxel in the test subject MR scan to patches in roughly the same location in one or more template MR scans (this is described in articles by F. Rousseau, P. A. Habas, and C. Studholme, entitled “A Supervised Patch-based Approach for Human Brain Labeling”, IEEE Transactions on Medical Imaging, 2011, pp. 1852-1862, Vol. 30(10) and P. Coupé, J. V. Manjón, V. Fonov, J. Pruessner, M. Robles, and D. L. Collins, entitled “Patch-based Segmentation using Expert Priors: Application to Hippocampus and Ventricle Segmentation”, NeuroImage, 2011, pp. 940-954, Vol. 54(2), each article being incorporated herein by reference). These methods rely on a measure of similarity between patches, such as correlation or square difference of intensity (see, for example, the Rousseau et al article above and an article by M. Sdika, entitled “Combining Atlas Based Segmentation and Intensity Classification with Nearest Neighbor Transform and Accuracy Weighted Vote”, Medical Image Analysis, 2010, pp. 219-226, Vol. 14(2)). Furthermore, as there might be multiple templates/atlases, similar patches across them must be combined, typically by a voting (see, for example, an article by X. Artaechevarria, A. Muñoz-Barrutia, and C. Ortiz-de-Solórzano, entitled “Combination Strategies in Multi-atlas Image Segmentation: Application to Brain MR Data”, IEEE Transactions on Medical Imaging, 2009, pp. 1266-1277, Vol. 28(8)) or non-local label fusion scheme (see, for example, the above articles by Rousseau et al. and Coupé et al.). The main advantage of the latter scheme is that input and atlas images don't have to match perfectly. Consequently, a rigid registration which reduces the time cost is sufficient (as noted in the above-cited Coupé et al. article).
A patch-based segmentation approach may be useful in allowing MR imaging to produce images with more clearly defined bone segmentation and, thus, in performing more accurate attenuation correction of PET imaging data.